Peer Alcohol Use Differentially Amplifies Genetic and Environmental Effects on Different Developmental Trajectories of Adolescent Alcohol Use

Journal of Adolescent Health xxx (2019) 1e8

www.jahonline.org Original article

Peer Alcohol Use Differentially Amplifies Genetic and Environmental Effects on Different Developmental Trajectories of Adolescent Alcohol Use Yao Zheng, Ph.D. a, *, Mara Brendgen, Ph.D. b, c, Alain Girard, M.Sc. c, Ginette Dionne, Ph.D. c, d, Michel Boivin, Ph.D. c, d, and Frank Vitaro, Ph.D. c, e a

Department of Psychology, University of Alberta, Edmonton, Alberta, Canada Department of Psychology, Université du Québec à Montréal, Montréal, Quebec, Canada c Research Unit for Children’s Psychosocial Maladjustment, Montréal, Quebec, Canada d Department of Psychology, Laval University, Québec City, Quebec, Canada e School of Psycho-Education, Université de Montréal, Québec City, Quebec, Canada b

Article history: Received April 30, 2019; Accepted July 6, 2019 Keywords: Adolescence; Alcohol use; Peer; Developmental trajectory; Gene-environment interaction

A B S T R A C T

Purpose: The purpose of the study was to investigate how peer alcohol use moderates genetic and environmental influences on three different developmental trajectories of alcohol use during adolescence: low (continuously low levels of use), early-onset fast-escalating (initiated use early, the level of use increased quickly), and normative increasing (started at a low level and increased steadily) using biometric modeling. Methods: Data were from a longitudinal study on a sample of population-based adolescent twins (N ¼ 842, 52.7% female, 84% European Caucasian). Adolescents self-reported past-year alcohol use at age 13, 14, 15, and 17 years. Adolescents’ nominated friends reported their own past-year alcohol use at age 13, 15, and 17 years. Results: Genetic and environmental influences on adolescents’ alcohol use trajectories were differentially moderated by friends’ alcohol use in different trajectories. Gene-environment interaction was implicated in the low and early-onset trajectories, such that genetic contributions were amplified when friends used more alcohol. Environment-environment interaction was involved in the normative increasing and early-onset trajectories, such that person-specific environmental contributions were amplified when friends’ alcohol use increased. Conclusions: Adolescent alcohol use remains a major public health issue, with peer alcohol use being a major risk factor. These findings suggest that close supervision to reduce deviant peer affiliation as well as preventions targeting peer group norms of alcohol use might be especially beneficial for adolescents following the normative increasing and early-onset trajectories. Ó 2019 Society for Adolescent Health and Medicine. All rights reserved.

Conflicts of interest: The authors have no conflicts of interest to disclose. * Address correspondence to: Yao Zheng, Ph.D., Department of Psychology, University of Alberta, P-217 Biological Sciences Building, Edmonton, AB, Canada, T6G 2E9. E-mail address: [email protected] (Y. Zheng). 1054-139X/Ó 2019 Society for Adolescent Health and Medicine. All rights reserved. https://doi.org/10.1016/j.jadohealth.2019.07.005

IMPLICATIONS AND CONTRIBUTION

This study investigated the moderation of peer alcohol use on genetic and environmental influences on different developmental trajectories of adolescent alcohol use. Different trajectories involved different gene-environment and environment-environment interaction mechanisms, informing future interventions to tailor potential targets for adolescents who follow distinct trajectories of alcohol use.

As a major public health problem, adolescent alcohol use, especially early onset and higher rates of growth, is associated with multiple concurrent and long-term negative outcomes [1,2]. Globally, alcohol use and related problems bring tremendous societal costs [3], calling for a better understanding of the

2

Y. Zheng et al. / Journal of Adolescent Health xxx (2019) 1e8

etiology and developmental processes of adolescent alcohol use for better intervention effects. Particularly, adolescents follow distinct developmental trajectories of alcohol use with different times of onset, levels, and rates of growth. Some consistently show low levels of alcohol use, whereas the majority starts at a low level and increases steadily throughout adolescence, representing relatively normative use; a small portion of adolescents follow a more problematic pattern with early onset, quick escalation, and high levels of use during adolescence [4e6]. Genetic and environmental factors both contribute to adolescent alcohol use [7]. Particularly, a common genetic factor largely contributes to the stability, whereas nonshared environmental factors (person-specific experiences that make siblings different) primarily contribute to change in adolescent alcohol use [8,9]. Furthermore, genetic and environmental factors differentially contribute to adolescents following distinct alcohol use trajectories: genetic influences are the smallest in the low trajectory; the normative increasing trajectory shows the largest nonshared environmental influences, and the low trajectory shows the smallest nonshared environmental influences; shared environmental (environmental experiences that make siblings similar to each other) influences are salient in the low and earlyonset fast-escalating trajectories but not in the normative increasing trajectory [6]. This finding highlights the importance of (1) identifying specific shared or nonshared environmental experiences that could distinguish distinct developmental trajectories of adolescent alcohol use and (2) examining how these environments could potentially modify genetic influences on trajectories. Peer alcohol use is robustly associated with adolescent alcohol use [10], which could be due to peer socialization or selection process. Peer socialization occurs through modeling and reinforcement by alcohol-using peers, whereas peer selection happens when adolescents choose peers with similar behaviors [11]. Both processes have been supported in previous studies [12e14]. Notably, peer selection process could partly be influenced by adolescents’ genetic predisposition through gene-environmental correlation (rGE) [15]. Adolescents genetically predisposed to alcohol use may actively select peers who also use alcohol, demonstrating common genetic factors between peer and adolescent alcohol use [16e23]. After controlling for common genetic and environmental factors, however, there remains an association between peer and adolescent alcohol use through nonshared environmental factors [17e19], suggesting that peer selection and socialization are at play. Peer socialization of alcohol use may also operate in interaction with genetic factors [7,15]. The behavioral expression of adolescents’ genetic predisposition to alcohol use may depend on peer alcohol use through gene-environment interaction (GE). Only four twin studies have investigated GE between peer and adolescent alcohol use, all cross-sectional. One Finnish study found that the expression of genetic predisposition in 14-year-old adolescents’ alcohol use increased when adolescents reported more peer deviance (peer alcohol, tobacco, drug use, and school problems) [24] and similarly in 17-year-old adolescents when they reported that more of their friends used alcohol [25]. Similar findings were reported among 12- to 21-year-old US adolescents and young adults (average age 16 years) using friends’ self-reported alcohol use [21,26]. Nevertheless, the importance and magnitude of the interaction between peer alcohol use and genetic factors on

adolescent alcohol use may vary across development [7,25]. Therefore, longitudinal studies are needed to investigate GE involving peer alcohol use across different developmental periods (e.g., from early to late adolescence) and in adolescents following different alcohol use trajectories (e.g., early starter, low user). To our knowledge, no study has examined how genetic and environmental influences interact with peer alcohol use to predict different alcohol use trajectories during adolescence. Elucidating GE mechanisms in different trajectories could inform future personalized interventions to tailor potential targets for adolescents following distinct alcohol use trajectories. To address this issue, this study aimed to extend a recent study using longitudinal data from a sample of population-based adolescent twins [6] to investigate whether and how peer alcohol use modifies genetic and environmental influences on three trajectories (i.e., low, early-onset fast-escalating, and normative increasing). Based on previous studies [21,24e26], we expected thateeafter controlling for any common genetic factors (i.e., rGE)eepeer alcohol use would amplify the expression of genetic predisposition to alcohol use. The moderation pattern, however, could differ across the three distinct trajectories, given that genetic and environmental factors differentially contributed to these trajectories [6]. Methods Sample and procedure The 421 twin pairs (73 monozygotic [MZ] males, 90 MZ females, 61 dizygotic [DZ] males, 67 DZ females, 130 DZ oppositesex) in a previous study [6] were part of a population-based sample of 662 MZ and DZ twin pairs from the greater Montréal area who were recruited at birth between November 1995 and July 1998 [27]. Zygosity was determined through genetic marker analyses, supplemented by diagnoses based on physical similarity using a questionnaire and chorionicity data. A subsample of same-sex twins with both genetic marker and physical similarity diagnoses showed a 96% correspondence rate. Genetic marker diagnoses were used whenever available, and physical similarity diagnoses were used in the absence of genetic marker diagnoses. Eighty-four percent of the families were of European descent, 3% were of African descent, 2% were of Asian descent, and 2% were Native Americans. The demographic characteristics of the families were comparable to the populations in the large urban centers of Québec province when the children were 5 months old [27]. This study used data collected from grades 7 through 11 when the children were 13, 14, 15, and 17 years, respectively. Overall average attrition was approximately 2% per year, such that 981 twins participated at least once from grades 7 through 11. In the previous study [6], only twins with at least two waves of data for both members of a pair were included in trajectory and twin analyses, rendering a total of 842 twins, who were included in the present study. Compared with those excluded families or lost due to attrition, families retained in analyses were more likely to be European descent, intact families, had higher annual total income. Data collection took place via personal interviews in the twin’s home. Active written consent from the twins and their parents was obtained. The study and procedure were approved by the Institutional Review Board of the University of Québec in Montréal and the Saint-Justine Hospital Research Center.

Y. Zheng et al. / Journal of Adolescent Health xxx (2019) 1e8 normative increasing (76.7%)

Measures

Analytic strategy A previous study with this sample [6] identified three trajectories of adolescent alcohol use using growth mixture modeling: low (15.1%), normative increasing (76.7%), and earlyonset fast-escalating (8.2%) groups (Figure 1). Univariate biometric liability threshold models were fit for each trajectory membership (operationalized as ordinalized posterior probability of belonging to each specific trajectory) to examine genetic and environmental contributions to the liability of belonging to identified trajectories [6]. Genetic contributions to the three trajectories were 27.6%, 37.7%, and 34.7%, respectively, whereas nonshared environmental contributions were 30.0%, 62.3%, and 43.8%, respectively. Shared environment only contributed to the low (42.4%) and early-onset fast-escalating (21.5%) trajectories. The present study directly used adolescents’ probability of following identified trajectories to investigate rGE and GE with FAU. We first fit a univariate biometric model to FAU to examine any genetic component (i.e., rGE). Biometric modeling decomposes the variance into additive genetic (A), shared environmental (C), and nonshared environmental (E) components (E also includes measurement error) [28]. For twins in the same pair, the correlation between A is 1 for MZ twins, because they share all their genes, and .5 for DZ twins because they share on average half of their segregating alleles. The correlation between C is 1 and 0 between E for both MZ and DZ twins [28]. Preliminary analyses suggested that the C component contributing to FAU was different between girls and boys among opposite-sex DZ twins. Thus, a qualitative sex-limitation biometric model was fit where, among opposite-sex DZ twins, the correlation between C was left to be freely estimated rather than fixed to 1 [20,29].

early-onset fast-escalating (8.2%)

3.5 3 2.5 2 1.5 1 0.5 0 13

Friends’ alcohol use. In grades 7, 9, and 11, adolescents were asked to provide name and contact information of up to five friends. These friends did not have to be school friends. Nominated friends were further contacted and invited to participate in the study. Four twins nominated six friends, who nevertheless were all invited. On average, in grades 7, 9, and 11, each adolescent twin had 1.51, 1.96, and 2.53 nominated friends, respectively, who also participated in the study. At each time, 57.1%, 67.5%, and 74.1% of twins, respectively, had at least one nominated friend who participated in the study, and 91.0% twins had at least one nominated friend who participated in at least one wave. Nominated friends self-reported their past-year alcohol use on the same 5-point item as described previously. At each time, the average score of friends’ alcohol use (FAU) was calculated across all participating friends, which was then averaged over all three times to create a total score of FAU. The number of nominated friends who participated was not correlated with FAU at either time.

low (15.1%)

4

Past Year Alcohol Use

Adolescent alcohol use. In grades 7, 8, 9, and 11, adolescents selfreported the frequency of past year alcohol use on a 5-point item (0 ¼ “I have not consumed alcohol in the past 12 months”, 1 ¼ “just once, to try”, 2 ¼ “less than one time per month”, 3 ¼ “about once a month”, 4 ¼ “one or two times a week or more”). The question was “during the past 12 months, how frequently have you consumed/drank alcohol?”, defining that one alcoholic drink is 4e5 oz of wine, or 10 oz beer, or 1e1.5 oz liquor, and that .5% beer does not count as alcohol.

3

14

15

16

17

Age in years

Figure 1. Developmental trajectories of alcohol use through adolescence. (Adapted from the study by Zheng et al. [6] with permission from Springer Nature.)

Gene-environment interaction between FAU and adolescents’ alcohol use trajectories were examined with multivariate biometric models. We specified a bivariate GE model [30] with the correlated-factors approach, which facilitates the examination of sex limitation [28]. In a correlated-factors biometric model (Figure 2), each variable (i.e., FAU and adolescents’ trajectory membership [TM]) is influenced by a separate genetic factor (AFAU and ATM). Correlations between genetic factors (rg) indicate the level of similarity between the genetic factors of the two variables. A similar structure was also specified for shared (CFAU and CTM) and nonshared environmental factors. The proportion of the correlation between the two variables due to common genetic, shared, and nonshared environmental factors, respectively, was computed by multiplying standardized paths on each variable and their correlation and then divided by the overall phenotypic correlation [28]. FAU was included as a moderator that affects genetic and environmental influences on each trajectory. Specifically, paths aTM, cTM, and eTM, which represent additive genetic, shared, and nonshared environmental influences on trajectory membership, respectively, can vary according to the level of FAU, as indicated by the regression coefficients ba, bc, and be. A significant b coefficient would suggest a moderating effect of FAU [31]. To facilitate interpretation, FAU was z-standardized; therefore, a value of 0 indicates the average level of FAU. All models were analyzed using structural equation modeling package OpenMx 2.0 [32] in R 3.4.1 with raw data maximum likelihood estimation to handle missing data. Parameter estimates, 95% confidence intervals (CIs), and model fit indices were provided. Goodness of fit was assessed with minus twice the log likelihood (2LL). Difference in 2LL between a full model and a nested model (reduced model with fewer parameters) was assessed by c2 difference tests, with the degrees of freedom equal to the difference in the number of parameters estimated between the full and the reduced model. A nonsignificant c2 test favors the reduced model as a more parsimonious model. Akaike information criterion was also computed, with a smaller value suggesting a better fit. Results FAU was positively and significantly correlated with adolescents’ own alcohol use at all times, rs ranging between .14 and .36 (Table 1). The sex-limitation biometric model showed that FAU

4

Y. Zheng et al. / Journal of Adolescent Health xxx (2019) 1e8

Figure 2. A correlated-factors nonscalar sex limitation biometric model for a pair of dizygotic opposite-sex (dos) twins with qualitative sex difference in shared environmental influences (rdos) on friends’ alcohol use (FAU). TM ¼ trajectory membership (i.e., ordinalized probability of following a trajectory). rg and rc represent within-twin cross-trait correlation for additive genetic (A) and shared environmental (C) factors, respectively. Only additive genetic factors (A) and shared environmental factors (C) are shown. Nonshared environmental factor (E) is omitted. Subscript (F, M) refers to the sex of the twin.

was largely under shared environmental influences (68.8%), with the remaining variance explained by nonshared environmental influences (31.2%). The correlation between the shared environmental factors between twins of opposite-sex pairs was significantly lower than 1.00, r ¼ .52, 95% CI (.25, .73). Multivariate biometric models revealed that FAU was modestly and positively correlated with the normative increasing trajectory, r ¼ .16, 95% CI (.09, .23) and moderately and negatively correlated with the low trajectory, r ¼ .31, 95% CI (.38, .23). FAU was not correlated with the early-onset trajectory, r ¼ .05, 95% CI (.03, .14). The negative correlation between FAU and the low trajectory was largely explained by common shared environmental factors, r ¼ .43, 95% CI (.45, .30) and by common nonshared environmental factors to a lesser degree, r ¼ .18, 95% CI (.31, .05). Thus, about 80% of

this correlation was explained by shared environmental factors. In contrast, the positive correlation between FAU and the normative increasing trajectory was entirely explained by common nonshared environmental factors, r ¼ .19, 95% CI (.09, .30). The left panel of Figure 3 depicts estimated genetic and environmental variance components, whereas the right panel depicts proportions of (i.e., standardized) genetic and environmental influences by FAU from 2 standard deviation (SD) below group average to 2 SD above group average for each trajectory based on estimates from the final model. As shown in Table 2, for the normative increasing trajectory, the nonshared environmental patheebut not the genetic patheewas moderated by FAU. The variance of nonshared environmental factors increased as FAU increased (Figure 3 top left). Consequently, as FAU increased, the proportion of nonshared environmental

Table 1 Descriptive statistics and correlations of alcohol use over time

Grade 8 Grade 9 Grade 11 Friends’ alcohol use M (SD)

Grade 7

Grade 8

Grade 9

Grade 11

Friends’ alcohol use

.45 .33 .20 .14 .30 (.65)

.53 .36 .17 .59 (.91)

.54 .29 1.16 (1.20)

.36 2.14 (1.34)

1.37 (.84)

All correlations are significant at .001 level. M ¼ mean; SD ¼ standard deviation.

Y. Zheng et al. / Journal of Adolescent Health xxx (2019) 1e8

5

Figure 3. Estimated variance components (left) and their proportions (right) for the liability of following normative increasing (top), low (middle), and early-onset (bottom) trajectories of adolescent alcohol use, respectively, as a function of their friends’ alcohol use.

influences increased, whereas heritability decreased (Figure 3 top right). For the low trajectory, only the genetic path was moderated such that the variance of genetic factors increased

substantially from negligible to predominant as FAU increased, whereas the variance of shared and nonshared environment factors remained stable (Figure 3 middle left). Hence, as FAU

6

Y. Zheng et al. / Journal of Adolescent Health xxx (2019) 1e8

Table 2 Friends’ alcohol use moderation of genetic and environmental influences on adolescents’ alcohol use trajectories

Normative A&E No A No E Low A, C, & E No A No C No E No C & E Early onset A, C, & E No A No C No E No A & C

2LL (df)

AIC

Dc2 (Ddf)

p

4267.01 (1484) 4267.70 (1485) 4280.74 (1485)

1299.01 1297.70 1310.74

e .69 (1) 13.73 (1)

e .40 .00

4008.98 4013.07 4010.00 4010.64 4011.61

(1477) (1478) (1478) (1478) (1479)

1054.98 1057.07 1054.00 1054.64 1053.61

e 4.09 (1) 1.02 (1) 1.66 (1) 2.63 (2)

e .04 .31 .20 .27

1597.58 1599.35 1597.64 1618.61 1607.55

(739) (740) (740) (740) (741)

119.58 119.35 117.64 138.61 125.55

e 1.76 (1) .06 (1) 21.02 (1) 9.97 (2)

e .18 .81 .00 .01

The final selected models were bolded. 2LL ¼ minus twice the log-likelihood; AIC ¼ Akaike information criteria.

increased, heritability increased, whereas the relative contributions of shared and nonshared environmental factors decreased (Figure 3 middle right). The early-onset trajectory showed a more complicated pattern. The model fit did not significantly worsen when dropping the interactions between FAU and either the genetic or the shared environmental path separately. Dropping both interactions together nevertheless led to a significantly worse model fit. Therefore, the model without the interaction involving the shared environmental path was selected as the best model, which also had the lowest Akaike information criterion (117.64). The interaction showed that, as FAU increased, the variance of genetic factors first decreased moderately, then increased substantially, whereas the variance of nonshared environmental factors increased continuously. Shared environmental variance remained modest and stable (Figure 3 bottom left). Correspondingly, heritability demonstrated a U-shape in that it was at its lowest level when FAU was about .5 SD below group average and increased as FAU either decreased or increased. The proportion of nonshared environmental influences escalated as FAU increased from low to average levels and remained stable thereafter. The proportion of shared environment was at its peak when FAU was 1 SD below group average and decreased as FAU increased (Figure 3 bottom right). Discussion This study examined whether FAU moderated genetic and environmental influences on adolescents’ developmental trajectories of alcohol use. There was no genetic influence on FAU, contrary to previous studies [16,17,20e22]. Instead, FAU was under substantial shared environmental influences, as well as nonshared environmental influences, albeit to a lesser degree. Other studies with similarly young participants also reported negligible to modest genetic influences but substantial shared environmental influences on peer alcohol use among adolescents [33] or only among girls (i.e., no genetic influence among boys) [18,23]. This finding may be due to the fact that we averaged FAU over time. Especially at age 13 and 14 years, the level of use was extremely low. Older adolescents might have had more time to select peers who are also increasingly using alcohol [13], thus gradually showing rGE [15]. Univariate results of FAU at each

time point in sensitivity analyses revealed that its genetic influences indeed increased over time from negligible or nonsignificant at grade 7 and 9 to moderate at grade 11 (Supplementary Materials). Despite substantial shared environmental influences on FAU, only about 25% of these environments were shared between opposite-sex twins. Finnish studies on 14-year-old twins’ own alcohol use also found that about 60% of the common environment was shared by boys and girls in opposite-sex pairs [34,35]. This finding suggests that family, school, and neighborhood influences on adolescents’ exposure to or affiliation with alcohol-using peers are equally important for both sexes, but that different aspects of these environments are at play for boys and girls. These environments may include gender-specific parenting (e.g., parental monitoring, gender socialization) [36] or peer relationship (e.g., sex differences in relationship styles, behavioral and social-cognitive aspects, stress, and coping) [37] during adolescence. Consistent with the literature [10], FAU was positively correlated with adolescents’ alcohol use at all times. Adolescents with peers who used more alcohol were more likely to follow the normative increasing trajectory and less likely to follow the low trajectory. Given the absence of genetic effects on FAU (i.e., no rGE), shared environmental factors largely accounted for the association between FAU and the low trajectory, whereas nonshared environmental factors fully accounted for the association between FAU and the normative increasing trajectory. Previous studies similarly showed that the links between adolescents’ and their peers’ alcohol use are explained to a considerable extent by shared or non-shared environmental pathways [18,20,33]. The early-onset trajectory was not associated with FAU, suggesting that peer alcohol use is not a primary risk factor for this relatively problematic drinking pattern. Adolescents following this trajectory may be more influenced by family factors, such as low parental supervision and permissive parental attitudes toward alcohol use [10,38]. For the normative increasing trajectory, there was no evidence of GE but an EE interaction, whereby FAU enhanced the effects of nonshared environmental factors on adolescents’ alcohol use [24,25]. Affiliation with peers who frequently use alcohol may influence adolescents’ attitudes to and perceived norms of alcohol use, exposure to social occasions and contexts that involve alcohol use, as well as access to alcohol. With the normative increasing trajectory being the most frequent pattern, this finding suggests that most adolescents will use alcohol, and increasingly so. It is the person-specific social opportunities and occasions (e.g., parties with drinking peers) that seem to trigger their alcohol use and lead them to follow this developmental trajectory. In contrast, GE was found for the low trajectory. Whereas genetic effects were negligible with low levels of FAU, they increased considerably as friends drank more alcohol. Note that this GE concerned the genetic influences on following a trajectory of no or low levels of alcohol use. This finding suggests that, when no or few peers use alcohol, environments primarily explain why some adolescents drink very little alcohol. However, as these adolescents encounter more peers who use alcohol, their genetic predisposition for not using alcohol is triggered. For instance, some adolescents may be genetically predisposed for alcohol aversion (e.g., cannot stand the smell or get nauseous after intake). Exposure to peers who use alcohol might further amplify their aversion against alcohol, thus pushing them to follow the low trajectory.

Y. Zheng et al. / Journal of Adolescent Health xxx (2019) 1e8

Both GE and EE were found for the early-onset trajectory. As FAU increased from very low levels, genetic influences first decreased moderately, then increased substantially, whereas nonshared environmental influences increased consistently. The increase of nonshared environmental effects may be explained by increased opportunities for drinking when socializing with other drinking peers. Exposure to drinking peers also further facilitates the expression of these adolescents’ genetic predisposition to alcohol use. By the same token, at very low levels of peer alcohol use (i.e., when opportunities for drinking with peers are low and access to alcohol is more difficult), their genetic predisposition to alcohol use may push them to seek out alternative ways to procure alcohol. In this case, these adolescents may drink without the company of peers or possibly in the company of family members instead of friends. This latter possibility is also suggested by the finding that the relative shared environmental contribution was at its peak when FAU was low. The current findings should be interpreted in the context of limitations. Particularly, we only used a single item to measure adolescents’ and their friends’ alcohol use. Although single items have been used in many previous studies with good validity [16,18,24e26], it would be more informative to measure multiple aspects of alcohol use (e.g., lifetime use, binge drinking). Second, our sample is primarily of European descent (84%). Therefore, the findings may not be directly generalized to other ethnic populations. More longitudinal twin studies of ethnically diverse samples are warranted. Third, the present study focused on the influences of peer alcohol use. Future research could also investigate family and parenting factors (e.g., parental knowledge) [24,25] that may differentially moderate genetic and environmental influences on different alcohol use trajectories. Nevertheless, this study also has a few notable strengths. The use of friends’ self-reports of alcohol use at multiple time points provided a more accurate assessment that could avoid the potential bias when using adolescents’ perceptions of their FAU. When available, we also assessed alcohol use of multiple friends, making the measure of FAU more inclusive and representative than studies focusing on alcohol use of the best friend only. Averaging FAU over time, rather than using FAU only at one time, allowed us to maximize statistical power in biometric moderation analyses. The results using overall FAU may be deemed robust and conservative, given that all moderation paths were replicated in sensitivity analyses when using only grade 9 or 11 FAU (Supplementary Materials). The longitudinal data from a population-based sample also allowed us to investigate genetic and environmental influences on distinct developmental trajectories of adolescent alcohol use. This study represents the first endeavor to extend previous cross-sectional studies [21,24e26] to investigate whether and how peer alcohol use interacts with genetic and environmental factors to predict distinct developmental trajectories of adolescent alcohol use. First, the findings highlight the potential value of leveraging genomic data, and more broadly biological information, in developmental and intervention research on adolescent alcohol use. While some adolescents are more genetically at risk for alcohol use, research has demonstrated that the same genetic predispositions also make them more responsive and malleable to intervention effects [39]. Future intervention research could consider incorporating genetically informed randomized control trials, such as microtrials, to identify different intervention components that are effective for people with different genetic risk and susceptibility [40]. Furthermore,

7

our findings showed that peer alcohol use, an amenable environmental risk factor, interacts with genetic and environmental factors in shaping distinct alcohol use trajectories. Nevertheless, different trajectories involved different GE and EE interaction mechanisms. For future intervention, the moderate to substantial shared environmental influences on the low and early-onset trajectories suggest that family-focused prevention against substance use could be particularly effective in delaying the onset of alcohol use. Particularly, interventions should tailor unique components to adolescent boys and girls to suit their genderspecific developmental and socialization processes, as they share unique common environmental factors that expose them to peers who use alcohol. Furthermore, given the EE interaction involving peer alcohol use observed for both the normative increasing and early-onset trajectories, close supervision to reduce deviant peer affiliation as well as preventions targeting peer group norms of alcohol use may be especially beneficial for adolescents following these trajectories. Acknowledgments The authors thank the twins and their families for participating in this study. Funding Sources This study was funded by the Canadian Institutes of Health Research (MOP 97882, MOP199546, and MOP 123342), the Fonds de Recherche du Québec (Société et Culture; 2014-JU-172894), and the Québec Ministry of Health and Social Services. Mara Brendgen was supported by a Senior Researcher Salary award from the Fonds de Recherche du Québéc sur la Société et la Culture. Michel Boivin was supported by the Canada Research Chair program from the Canadian Institutes of Health Research. The funding sources had no involvement in the study design, the collection, analysis and interpretation of data, the writing of the report, or the decision to submit the article for publication. Supplementary Data Supplementary data related to this article can be found at https://doi.org/10.1016/j.jadohealth.2019.07.005. References [1] Mason AW, Hitch JE, Kosterman R, et al. Growth in adolescent delinquency and alcohol use in relation to young adult crime, alcohol use disorders, and risky sex: A comparison of youth from low-versus middle-income backgrounds. J Child Psychol Psychiatry 2010;51:1377e85. [2] Odgers CL, Caspi A, Nagin DS, et al. Is it important to prevent early exposure to drugs and alcohol among adolescents? Psychol Sci 2008;19:1037e44. [3] Rehm J, Mathers C, Popova S, et al. Global burden of disease and injury and economic cost attributable to alcohol use and alcohol-use disorders. Lancet 2009;373:2223e33. [4] Flory K, Lynam D, Milich R, et al. Early adolescent through young adult alcohol and marijuana use trajectories: Early predictors, young adult outcomes, and predictive utility. Dev Psychopathol 2004;16:193e213. [5] Nelson SE, Van Ryzin MJ, Dishion TJ. Alcohol, marijuana, and tobacco use trajectories from age 12 to 24 years: Demographic correlates and young adult substance use problems. Dev Psychopathol 2015;27:253e77. [6] Zheng Y, Brendgen M, Dionne G, et al. Genetic and environmental influences on developmental trajectories of adolescent alcohol use. Eur Child Adolesc Psychiatry 2019. https://doi.org/10.1007/s00787-019-01284-x. [7] Dick DM. Developmental changes in genetic influences on alcohol use and dependence. Child Dev Perspect 2011;5:223e30.

8

Y. Zheng et al. / Journal of Adolescent Health xxx (2019) 1e8

[8] Long EC, Verhulst B, Aggen SH, et al. Contributions of genes and environment to developmental change in alcohol use. Behav Genet 2017;47: 498e506. [9] van Beek JH, Kendler KS, de Moor MH, et al. Stable genetic effects on symptoms of alcohol abuse and dependence from adolescence into early adulthood. Behav Genet 2012;42:40e56. [10] Hawkins JD, Catalano RF, Miller JY. Risk and protective factors for alcohol and other drug problems in adolescence and early adulthood: Implications for substance abuse prevention. Psychol Bull 1992;112:64e105. [11] Brechwald WA, Prinstein MJ. Beyond homophily: A decade of advances in understanding peer influence processes. J Res Adolesc 2011; 21:166e79. [12] Burk WJ, van der Vorst H, Kerr M, Stattin H. Alcohol use and friendship dynamics: Selection and socialization in early-, middle-, and lateadolescent peer networks. J Stud Alcohol Drugs 2012;73:89e98. [13] Mercken L, Steglich C, Knibbe R, de Vries H. Dynamics of friendship networks and alcohol use in early and mid-adolescence. J Stud Alcohol Drugs 2012;73:99e110. [14] Osgood DW, Ragan DT, Wallace L, et al. Peers and the emergence of alcohol use: Influence and selection processes in adolescent friendship networks. J Res Adolesc 2013;23:500e12. [15] Brendgen M. Genetics and peer relations: A review. J Res Adolesc 2012;22: 419e37. [16] Cleveland HH, Wiebe RP, Rowe DC. Sources of exposure to smoking and drinking friends among adolescents: A behavioral-genetic evaluation. J Genet Psychol 2005;166:153e69. [17] Cruz JE, Emery RE, Turkheimer E. Peer network drinking predicts increased alcohol use from adolescence to early adulthood after controlling for genetic and shared environmental selection. Dev Psychol 2012;48:1390e402. [18] Dick DM, Pagan JL, Holliday C, et al. Gender differences in friends’ influences on adolescent drinking: A genetic epidemiological study. Alcohol Clin Exp Res 2007;31:2012e9. [19] Edwards AC, Maes HH, Prescott CA, Kendler KS. Multiple mechanisms influencing the relationship between alcohol consumption and peer alcohol use. Alcohol Clin Exp Res 2015;39:324e32. [20] Fowler T, Shelton K, Lifford K, et al. Genetic and environmental influences on the relationship between peer alcohol use and own alcohol use in adolescents. Addiction 2007;102:894e903. [21] Harden KP, Hill JE, Turkfheimer E, Emery RE. Gene-environment correlation and interaction in peer effects on adolescent alcohol and tobacco use. Behav Genet 2008;38:339e47. [22] Hill J, Emery RE, Harden KP, et al. Alcohol use in adolescent twins and affiliation with substance using peers. J Abnorm Child Psychol 2008;36:81e94. [23] Loehlin JC. Is there an active gene-environment correlation in adolescent drinking behavior? Behav Genet 2010;40:447e51.

[24] Cooke ME, Meyers JL, Latvala A, et al. Geneeenvironment interaction effects of peer deviance, parental knowledge and stressful life events on adolescent alcohol use. Twin Res Hum Genet 2015;18:507e17. [25] Dick DM, Pagan JL, Viken R, et al. Changing environmental influences on substance use across development. Twin Res Hum Genet 2007;10:315e26. [26] Guo G, Elder GH, Cai T, Hamilton N. Geneeenvironment interactions: Peers’ alcohol use moderates genetic contribution to adolescent drinking behavior. Soc Sci Res 2009;38:213e24. [27] Boivin M, Brendgen M, Dionne G, et al. The Quebec newborn twin study into adolescence: 15 years later. Twin Res Hum Genet 2012;16:64e9. [28] Neale MC, Maes HHM. Methodology for genetic studies of twins and families. Dordrecht, The Netherlands: Kluwer Academic Publishers; 2004. [29] Neale MC, Roysamb E, Jacobson K. Multivariate genetic analysis of sex limitation and GE interaction. Twin Res Hum Genet 2006;9:481e9. [30] Van der Sluis S, Posthuma D, Dolan CV. A note on false positives and power in GE modelling of twin data. Behav Genet 2012;42:170e86. [31] Purcell S. Variance components models for geneeenvironment interaction in twin analysis. Twin Res Hum Genet 2002;5:554e71. [32] Neale MC, Hunter MD, Pritikin JN, et al. OpenMx 2.0: Extended structural equation and statistical modeling. Psychometrika 2016;81:535e49. [33] Walden B, McGue M, Iacono WG, et al. Identifying shared environmental contributions to early substance use: The respective roles of peers and parents. J Abnorm Psychol 2004;113:440e50. [34] Rose RJ, Dick DM, Viken RJ, Kaprio J. Gene-environment interaction in patterns of adolescent drinking: Regional residency moderates longitudinal influences on alcohol use. Alcohol Clin Exp Res 2001;25:637e43. [35] Viken RJ, Kaprio J, Koskenvuo M, Rose RJ. Longitudinal analyses of the determinants of drinking and of drinking to intoxication in adolescent twins. Behav Genet 1999;29:455e61. [36] Schulte MT, Ramo D, Brown SA. Gender differences in factors influencing alcohol use and drinking progression among adolescents. Clin Psychol Rev 2009;29:535e47. [37] Rose AJ, Rudolph KD. A review of sex differences in peer relationship processes: Potential trade-offs for the emotional and behavioral development of girls and boys. Psychol Bull 2006;132:98e131. [38] van der Vorst H, Engels RC, Meeus W, et al. The role of alcohol-specific socialization in adolescents’ drinking behaviour. Addiction 2005;100: 1464e76. [39] Zheng Y, Albert D, McMahon RJ, et al. Glucocorticoid receptor (NR3C1) gene polymorphism moderate intervention effects on the developmental trajectory of African-American adolescent alcohol abuse. Prev Sci 2018;19: 79e89. [40] Brody GH, Beach SR, Hill KG, et al. Using genetically informed, randomized prevention trials to test etiological hypotheses about child and adolescent drug use and psychopathology. Am J Public Health 2013;103:S19e24.